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eac1c63 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | #!/usr/bin/env python3
"""
Download SFW Safebooru samples with known tags and benchmark the owned
InferenceEngine vs imgutils baseline.
Usage (from backend/):
../.venv/Scripts/python.exe scripts/bench_sfw_sample.py
"""
from __future__ import annotations
import json
import sys
import time
import urllib.parse
import urllib.request
from pathlib import Path
ROOT = Path(__file__).resolve().parents[2]
SAMPLE_DIR = ROOT / "sample_data" / "sfw_safebooru"
META_PATH = SAMPLE_DIR / "manifest.json"
API = "https://safebooru.org/index.php"
QUERY = "1girl solo rating:safe"
LIMIT = 6
def _http_json(url: str) -> object:
request = urllib.request.Request(
url,
headers={"User-Agent": "thr3shr-bench/1.0"},
)
with urllib.request.urlopen(request, timeout=90) as resp:
return json.loads(resp.read().decode("utf-8"))
def _download(url: str, dest: Path) -> None:
request = urllib.request.Request(
url,
headers={"User-Agent": "thr3shr-bench/1.0"},
)
with urllib.request.urlopen(request, timeout=120) as resp:
dest.write_bytes(resp.read())
def fetch_samples() -> list[dict]:
SAMPLE_DIR.mkdir(parents=True, exist_ok=True)
if META_PATH.exists():
return json.loads(META_PATH.read_text(encoding="utf-8"))
params = urllib.parse.urlencode(
{
"page": "dapi",
"s": "post",
"q": "index",
"json": "1",
"limit": str(LIMIT),
"tags": QUERY,
}
)
posts = _http_json(f"{API}?{params}")
if not isinstance(posts, list) or not posts:
raise RuntimeError("No Safebooru posts returned")
manifest: list[dict] = []
for post in posts:
file_url = post.get("sample_url") or post.get("file_url")
if not file_url:
continue
post_id = post["id"]
ext = Path(str(file_url)).suffix or ".jpg"
dest = SAMPLE_DIR / f"{post_id}{ext}"
if not dest.exists():
print(f"downloading {post_id} …", flush=True)
_download(str(file_url), dest)
tags = str(post.get("tags") or "").split()
manifest.append(
{
"id": post_id,
"path": str(dest),
"rating": post.get("rating"),
"known_general": tags,
"must_have": [t for t in ("1girl", "solo") if t in tags],
}
)
META_PATH.write_text(json.dumps(manifest, indent=2), encoding="utf-8")
return manifest
def top_n(scores: dict[str, float], n: int = 10) -> list[tuple[str, float]]:
return sorted(scores.items(), key=lambda item: (-item[1], item[0]))[:n]
def main() -> int:
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from imgutils.tagging import get_wd14_tags
from app.inference_engine import InferenceEngine, reset_engine
from app.services import _parse_wd14_raw
manifest = fetch_samples()
print(f"samples={len(manifest)} dir={SAMPLE_DIR}")
for row in manifest:
rating = str(row.get("rating") or "").lower()
assert rating in {"safe", "s", "g", "general", ""}, rating
print(f" #{row['id']} must_have={row['must_have']} file={Path(row['path']).name}")
paths = [Path(row["path"]) for row in manifest]
reset_engine()
engine = InferenceEngine()
engine.warm("wd_swinv2_v3")
t0 = time.perf_counter()
baseline: list[dict[str, float]] = []
for path in paths:
raw = get_wd14_tags(
str(path),
model_name="SwinV2_v3",
general_threshold=0.35,
no_underline=False,
drop_overlap=False,
fmt="general",
)
baseline.append(_parse_wd14_raw(raw))
baseline_s = time.perf_counter() - t0
t0 = time.perf_counter()
serial = [
engine.score_one(path, tagger_model="wd_swinv2_v3", wd_general_threshold=0.35)
for path in paths
]
serial_s = time.perf_counter() - t0
t0 = time.perf_counter()
batched = engine.score_many(
paths,
tagger_model="wd_swinv2_v3",
wd_general_threshold=0.35,
batch_size=min(4, len(paths)),
)
batch_s = time.perf_counter() - t0
print("\n=== Latency ===")
print(f"imgutils serial : {baseline_s:.3f}s ({baseline_s / len(paths) * 1000:.1f} ms/img)")
print(f"engine serial : {serial_s:.3f}s ({serial_s / len(paths) * 1000:.1f} ms/img)")
print(f"engine batch : {batch_s:.3f}s ({batch_s / len(paths) * 1000:.1f} ms/img)")
if batch_s > 0:
print(f"speedup vs imgutils: {baseline_s / batch_s:.2f}x")
print("\n=== Known-tag recall + equivalence ===")
equiv_failures = 0
recall_hits = 0
recall_total = 0
for row, base, eng_s, eng_b in zip(manifest, baseline, serial, batched):
must = set(row["must_have"])
recall_total += 1
hit_s = must <= set(eng_s)
hit_b = must <= set(eng_b)
if hit_s and hit_b:
recall_hits += 1
shared = set(base) & set(eng_s)
max_delta = max((abs(base[t] - eng_s[t]) for t in shared), default=0.0)
# Also require identical tag sets vs imgutils at this threshold.
set_match = set(base) == set(eng_s) == set(eng_b)
ok = set_match and max_delta < 1e-3
status = "OK" if ok else "FAIL"
if not ok:
equiv_failures += 1
print(
f" #{row['id']} equiv={status} known_hit={hit_s and hit_b} "
f"max_delta={max_delta:.6f} top={top_n(eng_b, 5)}"
)
print(
f"\nknown-tag recall@{0.35}: {recall_hits}/{recall_total} "
f"(model may miss site tags; equivalence is the gate)"
)
print(f"equivalence failures: {equiv_failures}")
return 1 if equiv_failures else 0
if __name__ == "__main__":
raise SystemExit(main())
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